Abstract: An image containing one or more types of objects to be located is analyzed to locate linear features within the image. The objects have edges having known spatial relationships. The linear features and identified virtual lines are analyzed to find groups of linear features and/or virtual lines that have one of the known spatial relationships. These relationships can include parallel edges, edges that meet at certain angles or angle ranges, the number of lines meeting a vertex and the like. The identified group is compared with projected 2-dimensional representation(s) of the object(s) to determine whether any additional lines appear in the image that are part of the located object. In various exemplary embodiments, two or more hypotheses for how the identified group of linear features maps to the 3-dimensional representation of the object can be generated. The best fitting hypothesis becomes the recognized 3-dimensional shape and orientation for that object.

Abstract: An image containing one or more types of objects to be located is analyzed to locate linear features within the image. The objects have edges having known spatial relationships. The linear features and identified virtual lines are analyzed to find groups of linear features and/or virtual lines that have one of the known spatial relationships. These relationships can include parallel edges, edges that meet at certain angles or angle ranges, the number of lines meeting a vertex and the like. The identified group is compared with projected 2-dimensional representation(s) of the object(s) to determine whether any additional lines appear in the image that are part of the located object. In various exemplary embodiments, two or more hypotheses for how the identified group of linear features maps to the 3-dimensional representation of the object can be generated. The best fitting hypothesis becomes the recognized 3-dimensional shape and orientation for that object.

Abstract: An image containing one or more types of objects to be located is analyzed to locate linear features within the image. The objects have edges having known spatial relationships. The linear features and identified virtual lines are analyzed to find groups of linear features and/or virtual lines that have one of the known spatial relationships. These relationships can include parallel edges, edges that meet at certain angles or angle ranges, the number of lines meeting a vertex and the like. The identified group is compared with projected 2-dimensional representation(s) of the object(s) to determine whether any additional lines appear in the image that are part of the located object. In various exemplary embodiments, two or more hypotheses for how the identified group of linear features maps to the 3-dimensional representation of the object can be generated. The best fitting hypothesis becomes the recognized 3-dimensional shape and orientation for that object.

Abstract: An image of high-aspect-ratio objects is analyzed to locate linear features within the image. The gradient direction for each pixel is determined, and connected pixels having similar gradient directions are grouped into line support regions. A linear feature is determined for each line support region. The linear features are analyzed to identify those that are co-linear. The linear features are then analyzed to find groups that are parallel and that sufficiently overlap. Additional sets of linear features that intersect the identified group are combined into that group. The line support regions of the grouped linear features are analyzed to determine representative orientation and/or dimensional information, which is analyzed to determine statistical information about a plurality of the objects. This information is used to monitor or control one or more processes associated with the objects, to reject the objects or a structure or device comprising the objects, or the like.

Abstract: A partial enumeration model predictive controller and method of predictive control for a multiple input, multiple output (MIMO) system, including providing a solution table with problem solutions to a model predictive control problem for the MIMO system over a partial parameter region; scanning the solution table for an optimal solution for current parameters; using the optimal solution to control the MIMO system when the optimal solution is in the solution table; and using an alternative solution to control the MIMO system when the optimal solution is not in the solution table.

Abstract: A method of predictive control for a single input, single output (SISO) system, including modeling the SISO system with model factors, detecting output from the SISO system, estimating a filtered disturbance from the output, determining a steady state target state from the filtered disturbance and a steady state target output, populating a dynamic optimization solution table using the model factors and a main tuning parameter, and determining an optimum input from the dynamic optimization solution table. Determining an optimum input includes determining a time varying parameter, determining a potential optimum input from the time varying parameter, and checking whether the potential optimum input is the optimum input.

Abstract: A partial enumeration model predictive controller and method of predictive control for a multiple input, multiple output (MIMO) system, including providing a solution table with problem solutions to a model predictive control problem for the MIMO system over a partial parameter region; scanning the solution table for an optimal solution for current parameters; using the optimal solution to control the MIMO system when the optimal solution is in the solution table; and using an alternative solution to control the MIMO system when the optimal solution is not in the solution table.

Abstract: An image containing one or more types of objects to be located is analyzed to locate linear features within the image. The objects have edges having known spatial relationships. The linear features and identified virtual lines are analyzed to find groups of linear features and/or virtual lines that have one of the known spatial relationships. These relationships can include parallel edges, edges that meet at certain angles or angle ranges, the number of lines meeting a vertex and the like. The identified group is compared with projected 2-dimensional representation(s) of the object(s) to determine whether any additional lines appear in the image that are part of the located object. In various exemplary embodiments, two or more hypotheses for how the identified group of linear features maps to the 3-dimensional representation of the object can be generated. The best fitting hypothesis becomes the recognized 3-dimensional shape and orientation for that object.